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# Copyright 2018 Kornia Team
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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# distributed under the License is distributed on an "AS IS" BASIS,
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from typing import Optional

from kornia.models._hf_models import HFONNXComunnityModelLoader

from .base import DepthEstimation

__all__ = ["DepthAnythingONNXBuilder"]


class DepthAnythingONNXBuilder:
    """Provide static methods to build DepthAnything models for ONNX inference."""

    @staticmethod
    def build(
        model_name: str = "depth-anything-v2-small", model_type: str = "model", cache_dir: Optional[str] = None
    ) -> DepthEstimation:
        """Export a DepthAnything model to an ONNX model file.

        Args:
            model_name: The name of the model to be loaded. Valid model names include:
                - `depth-anything-v2-small`
                - `depth-anything-v2-base`
                - `depth-anything-v2-large`
            model_type:
                The type of the model to be loaded. Valid model types include:
                    - `model`
                    - `model_bnb4`
                    - `model_fp16`
                    - `model_int8`
                    - `model_q4`
                    - `model_quantized`
                    - `model_uint8`
            cache_dir:
                The directory where the model should be cached.

        Returns:
            DepthEstimation: The depth estimation model.

        Note:
            To use this model, load an image tensor (shape: ``(1, C, H, W)``) and call ``model.save(image)``.

        """
        if model_name not in [
            "depth-anything-v2-small",
            "depth-anything-v2-base",
            "depth-anything-v2-large",
        ]:
            raise ValueError(f"{model_name} is not a valid model name.")
        loader = HFONNXComunnityModelLoader(model_name, model_type=model_type, cache_dir=cache_dir)
        onnx_model = loader.load_model(
            download=True,
            io_name_mapping={"pixel_values": "input", "predicted_depth": "output"},
        )
        preproc = loader.load_preprocessing().to_onnx(save=False)
        return DepthEstimation(onnx_model, pre_processor=preproc, name=f"{model_name}_{model_type}")
